library(QFeatures)
library(msqrob2)
library(dplyr)
library(tidyr)
library(tibble)
library(gt)
library(plotly)
library(stageR)
library(poolr)
library(RColorBrewer)
library(seqinr)
library(stringr)
library(ExploreModelMatrix)
library(data.table)

1 Import data

df <- read.csv("evidence.txt",header=TRUE, sep = ",")
#I can ignore the sigma proteins (those were just used for QC purposes)
df <- df %>% filter(!grepl("Sigma", df$Proteins))
#filter out phospho sites with less than 75% probability
#(other filtering options are possible)
filter_rows <- sapply(df$Phospho..STY..Probabilities, function(x){
  if (!x==""){
    prob <- str_extract_all(x,  "(?<=\\().+?(?=\\))")[[1]]
    if (!any(as.double(prob) > 0.75)){return(which(df$Phospho..STY..Probabilities == x))}
  }
}, USE.NAMES = F)
filter_rows <- unique(unlist(filter_rows))
nrow(df)
## [1] 178235
df <- df[-filter_rows,]
nrow(df)
## [1] 171780
#Now get format into wide format
df_wide <- pivot_wider(df, id_cols = c("Sequence", "Modifications", "Modified.sequence", "Proteins", "Leading.proteins",
                                        "Reverse", "Potential.contaminant", "Protein.group.IDs", "Leading.razor.protein"),
                                                                          #Is dat hier de beste manier om met 
                                                                          #gedupliceerde features om te gaan?
                       names_from = "Raw.file", names_prefix = "Intensity_", values_from = "Intensity", values_fn = max)
ecols <- grep("Intensity",colnames(df_wide))
#order dataframe by protein, for the normalisation step
df_wide = df_wide[order(df_wide$Leading.razor.protein),]
pe <- readQFeatures(df_wide,ecol= ecols,name="peptidoformRaw")
rownames(pe[["peptidoformRaw"]]) <- rowData(pe[["peptidoformRaw"]])$Modified.sequence

1.1 Experimental Layout

metadata <- read.csv("Experimental Design.csv")
colData(pe)$file <- sapply(str_split(rownames(colData(pe)), "_"), function(x) x[[2]])
metadata$File <- sub(x=metadata$File, pattern=".raw",replacement = "")
colData(pe)$condition <- sapply(colData(pe)$file, function(x){
  metadata[metadata$File==x,]$Condition
}, USE.NAMES = F)
colData(pe)$subset <- sapply(colData(pe)$file, function(x){
  metadata[metadata$File==x,]$Subset
}, USE.NAMES = F)
colData(pe)$condition <- case_when(grepl("A", colData(pe)$condition) ~ "A",
                                        TRUE ~ "B")
colData(pe)
## DataFrame with 90 rows and 3 columns
##                            file   condition      subset
##                     <character> <character> <character>
## Intensity_QX21595GM   QX21595GM           A           y
## Intensity_QX21643GM   QX21643GM           A           y
## Intensity_QX21469GM   QX21469GM           B           x
## Intensity_QX21484GM   QX21484GM           B           x
## Intensity_QX21487GM   QX21487GM           A           x
## ...                         ...         ...         ...
## Intensity_QX21782GM   QX21782GM           A           y
## Intensity_QX21794GM   QX21794GM           A           y
## Intensity_QX21803GM   QX21803GM           B           y
## Intensity_QX21806GM   QX21806GM           A           y
## Intensity_QX21812GM   QX21812GM           A           y
MSnbase::plotNA(assay(pe[["peptidoformRaw"]])) +
  xlab("Peptide index (ordered by data completeness)")
## Warning in fun(libname, pkgname): mzR has been built against a different Rcpp version (1.0.8.3)
## than is installed on your system (1.0.9). This might lead to errors
## when loading mzR. If you encounter such issues, please send a report,
## including the output of sessionInfo() to the Bioc support forum at 
## https://support.bioconductor.org/. For details see also
## https://github.com/sneumann/mzR/wiki/mzR-Rcpp-compiler-linker-issue.

2 Preprocessing

rowData(pe[["peptidoformRaw"]])$nNonZero <- rowSums(assay(pe[["peptidoformRaw"]]) > 0, na.rm = T)
pe <- zeroIsNA(pe, i = "peptidoformRaw")
pe <- filterFeatures(pe, ~Reverse != "+")
pe <- filterFeatures(pe, ~Potential.contaminant != "+")
pe <- logTransform(pe, base = 2, i = "peptidoformRaw", name = "peptidoformLog")
pe <- pe[rowData(pe[["peptidoformRaw"]])$nNonZero>2,,]
pe <- QFeatures::normalize(pe, method = "center.median", i = "peptidoformLog", name = "peptidoform")
colData(pe[["peptidoform"]]) <- colData(pe)

2.1 Normalisation via robust summarisation

pe <- aggregateFeatures(pe,
 i = "peptidoform",
 fcol = "Leading.razor.protein",
 na.rm = TRUE,
 name = "proteinRobust",
 fun = MsCoreUtils::robustSummary)
## Your quantitative data contain missing values. Please read the relevant
## section(s) in the aggregateFeatures manual page regarding the effects
## of missing values on data aggregation.
#Only take pepforms that have a parent protein
#(when there is no global profiling dataset, this will be all peptidoforms)
pepWithProtein <- rowData(pe[["peptidoform"]])$Proteins %in% rownames(pe[["proteinRobust"]])
pePepWithProtein  <- pe[["peptidoform"]][pepWithProtein,]
pe <- addAssay(pe,pePepWithProtein,"pepformRel")
#normalisation for protein abundance step
assay(pe[["pepformRel"]]) <- assay(pe[["pepformRel"]]) - assay(pe[["proteinRobust"]])[rowData(pe[["pepformRel"]])$Proteins,colnames(assay(pe[["pepformRel"]]))]
boxplot(assay(pe[["pepformRel"]]))

3 Differential peptidoform usage (DPFU)

3.1 Hypothesistest for each contrast

colData(pe)$condition <- relevel(as.factor(colData(pe)$condition), ref = "B")
colData(pe)$subset <- as.factor(colData(pe)$subset)
pe <- msqrob2::msqrob(object = pe, i = "pepformRel", formula = ~condition*subset, robust=FALSE)
## Warning: Zero sample variances detected, have been offset away from zero
rowData(pe[["pepformRel"]])$msqrobModels[[2]] %>%
                  getCoef
##        (Intercept)         conditionA            subsety conditionA:subsety 
##         -0.9008228          1.3425797          0.9384266         -1.1383736
fraction_of_ys <- (colData(pe) %>% as_tibble() %>% filter(subset == "y") %>% nrow()) / nrow(colData(pe))
contrasts <- c("conditionA", "conditionA + conditionA:subsety", "conditionA:subsety", "conditionA + 0.5 * conditionA:subsety", "conditionA + 0.6666667 * conditionA:subsety")
L <- makeContrast(c("conditionA = 0",
                    "conditionA + conditionA:subsety = 0",
                    "conditionA:subsety = 0",
                    "conditionA + 0.5 * conditionA:subsety = 0",
                    "conditionA + 0.6666667 * conditionA:subsety = 0"),
                  parameterNames = rowData(pe[["pepformRel"]])$msqrobModels[[2]] %>%
                  getCoef %>%
                  names)
pe <- hypothesisTest(object = pe, i = "pepformRel", contrast = L, overwrite = T)

3.1.1 Volcanoplot

volcano <- list()
for (contrast in contrasts){
volcano[[contrast]] <- rowData(pe[["pepformRel"]])[[contrast]]%>%
            ggplot(aes(x = logFC, y = -log10(pval), 
                   color = adjPval < 0.05,
                   annotation=rowData(pe[["pepformRel"]])[,3])) +
  geom_point(cex = 2.5) +
  scale_color_manual(values = alpha(c("black", "red"), 0.5)) + 
  theme_minimal() +
  ylab("-log10(pvalue)") +
  ggtitle(contrast)
}
volcano
## $conditionA
## Warning: Removed 687 rows containing missing values (geom_point).

## 
## $`conditionA + conditionA:subsety`
## Warning: Removed 687 rows containing missing values (geom_point).

## 
## $`conditionA:subsety`
## Warning: Removed 687 rows containing missing values (geom_point).

## 
## $`conditionA + 0.5 * conditionA:subsety`
## Warning: Removed 687 rows containing missing values (geom_point).

## 
## $`conditionA + 0.6666667 * conditionA:subsety`
## Warning: Removed 687 rows containing missing values (geom_point).

3.1.2 Significant peptidoforms for each contrast

tables <- list()
for (contrast in contrasts){
sigTable <- rowData(pe[["pepformRel"]])[[contrast]]
if(nrow(sigTable %>%
  na.exclude %>%
  filter(adjPval<0.05))>0){
sigTable <- sigTable %>%
  na.exclude %>%
  filter(adjPval<0.05) %>%
  arrange(pval) %>%
  mutate(
    se = format(se, digits = 2), 
    df = format(df, digits =2),
    t = format(t, digits = 2),
    adjPval = format(adjPval, digits = 3),
    rank = 1:length(logFC) 
  ) 
sigTable_print <- sigTable %>% mutate(peptidoform = rownames(sigTable)) %>% gt() %>% tab_header(title = md(contrast))
tables[[contrast]] <- sigTable
}
}
knitr::kable(tables)
logFC se df t pval adjPval rank
VGYVSGWGR -1.447473 0.3 37 -4.8 2.98e-05 0.0463 1
logFC se df t pval adjPval rank
VS(Phospho (STY))REFHSHEFHSHEDM(Oxidation (M))LVVDPK -1.3149071 0.30 71.6 -4.3 0.0000450 0.0361 1
EPQDTYHYLPFS(Phospho (STY))LPHR -1.6567618 0.36 34.6 -4.6 0.0000563 0.0361 2
LPIVNFDYS(Phospho (STY))M(Oxidation (M))EEK -0.7819471 0.19 75.6 -4.2 0.0000697 0.0361 3
LNVEDVDSTK 0.6265500 0.15 49.6 4.2 0.0001015 0.0395 4
AAM(Oxidation (M))VGMLANFLGFR -3.1281305 0.19 3.6 -16.4 0.0001530 0.0476 5
ADQTVLTEDEK 1.0553334 0.27 70.6 3.9 0.0001927 0.0500 6
logFC se df t pval adjPval rank
VGYVSGWGR -0.8150185 0.17 37 -4.9 2.01e-05 0.0313 1
logFC se df t pval adjPval rank
LPIVNFDYS(Phospho (STY))M(Oxidation (M))EEK -0.7089448 0.15 75.6 -4.6 0.0000153 0.0238 1
VGYVSGWGR -0.6042003 0.14 36.6 -4.4 0.0000812 0.0494 2
VS(Phospho (STY))REFHSHEFHSHEDM(Oxidation (M))LVVDPK -0.9902020 0.24 71.6 -4.1 0.0001164 0.0494 3
AAM(Oxidation (M))VGMLANFLGFR -2.4859751 0.14 3.6 -17.3 0.0001270 0.0494 4

4 DPTM

4.1 ptm summarisatie

4.1.1 Determine location of ptm

fasta <- "human.fasta"
parsed_fasta <- read.fasta(file = fasta, seqtype = "AA", as.string = T)
#modified sequence column contains _ that does not matter, but hinders location determining
rowData(pe[["pepformRel"]])$modified_sequence <- gsub("_", "", rowData(pe[["pepformRel"]])$Modified.sequence)
get_ptm_location <- function(feature, data, fasta, mod_column = "Modifications", 
                             peptide_seq_column = "Sequence", mod_seq_column = "modified_sequence", 
                             protein_column = "Leading.razor.protein", split = ",", collapse = ", "){
  prot <- data[feature,][[protein_column]]
  pep_seq <- data[feature,][[peptide_seq_column]]
  mod_seq <- data[feature,][[mod_seq_column]]
  prot_seq <- fasta[[prot]][1]

  #find location of peptide in protein
  #add -1 here, so that the addition of the location later on is correct
  pep_location <- unlist(lapply(gregexpr(pattern = pep_seq, prot_seq), min)) - 1
  final_mod <- c()
  j <- mod_seq
  #go over the modifications inside the modified sequence
  for(mod in regmatches(mod_seq, gregexpr("\\(.*?\\)\\)", mod_seq, perl=T))[[1]]){
    #find location of modification in peptide
    mod_location <- unlist(lapply(gregexpr(pattern = mod, j, fixed = T), min))
    #get location in protein (-1 because else it gives you the location after because of the presence of the modification in the string)
    location <- mod_location + pep_location -1
    #add location to modification
    mod_ <- paste(mod, location)
    #save modification
    final_mod <- c(final_mod, mod_)
    #now remove current modification from the sequence, so that we can continue to the next mod
    str_sub(j, mod_location, nchar(mod)+mod_location-1) <- ""
  }
  return(paste(final_mod, collapse = collapse))
}
rowData(pe[["pepformRel"]])$mod <- sapply(rownames(rowData(pe[["pepformRel"]])), function(x){
    get_ptm_location(x, rowData(pe[["pepformRel"]]), parsed_fasta)
}, USE.NAMES = F)
#Add ptm variable = protein + modification
rowData(pe[["pepformRel"]])$ptm <- ifelse(rowData(pe[["pepformRel"]])$mod != "",
                                               paste(rowData(pe[["pepformRel"]])$Leading.razor.protein, rowData(pe[["pepformRel"]])$mod, sep="_"), 
                                               "")

4.1.2 Get ptm level intensity matrix

Get all unique ptms present in the dataset (all protein - modification - location combinations)

prots <- unique(rowData(pe[["pepformRel"]])$Leading.razor.protein)
#Do for each protein
ptms <- sapply(prots, function(i) {
  pe_sub <- pe[["pepformRel"]][grepl(i, rowData(pe[["pepformRel"]])$Leading.razor.protein, fixed = T),]
  #pe_sub <- filterFeatures(pe,~grepl(Leading.razor.protein,pattern=i,fixed = T))
  #Get all unique modification present on that protein
  mods <- unique(unlist(strsplit(rowData(pe_sub)$mod, split = ", ", fixed = TRUE)))
  #Add protein info to mods
  ptm <- paste(rep(i, length(mods)), mods)
  #return all the protein-mods combinations
  ptm
})
ptms <- as.vector(unlist(ptms))
#For each ptm do
ptm_x_assay <- sapply(seq(1:length(ptms)), function(i){ 
  x <- ptms[i]
  #Get current protein and mod from ptm
  prot <- str_split(x, " ", 2)[[1]][1]
  current_ptm <- str_split(x, " ", 2)[[1]][2]
  #filter on that protein and on that mod to obtain all peptidoforms that correspond to the ptm
  pe_sub <- pe[["pepformRel"]][grepl(prot, rowData(pe[["pepformRel"]])$Leading.razor.protein, fixed = T),]
  #pe_sub <- filterFeatures(pe,~grepl(Leading.razor.protein,pattern=prot, fixed = T))
  ptm_sub <- pe_sub[grepl(current_ptm, rowData(pe_sub)$mod, fixed = T),]
  #ptm_sub <- filterFeatures(pe_sub,~grepl(mod,pattern=current_ptm, fixed=T))[["peptidoformNorm"]]
  #Get intensity values of those peptidoforms
  y <- assay(ptm_sub)
  #And summarise them to 1 row of intensity values: 1 value per sample for that ptm
  ptm_y <- try(MsCoreUtils::robustSummary(y), silent = T)
  if (is(ptm_y, "try-error")){
    ptm_y <- rep(NA, ncol(y))}
  ptm_y
})
## Warning in rlm.default(X, expression, ...): 'rlm' failed to converge in 20 steps

## Warning in rlm.default(X, expression, ...): 'rlm' failed to converge in 20 steps

## Warning in rlm.default(X, expression, ...): 'rlm' failed to converge in 20 steps

## Warning in rlm.default(X, expression, ...): 'rlm' failed to converge in 20 steps
#Then we get the intensity assay on ptm level
ptm_x_assay <- t(ptm_x_assay)
rownames(ptm_x_assay) <- ptms

4.1.3 Add to QFeatures object

Filter out ptms with all zero intensities

print(paste(nrow(ptm_x_assay), "ptms before filtering"))
## [1] "610 ptms before filtering"
filtering <- rowSums(ptm_x_assay != 0, na.rm=TRUE) > 0 
ptm_x_assay <- ptm_x_assay[filtering,]
print(paste(nrow(ptm_x_assay), "ptms after filtering"))
## [1] "471 ptms after filtering"
all(rownames(colData(pe)) == colnames(ptm_x_assay))
## [1] TRUE
rowdata <- data.frame(ptm = rownames(ptm_x_assay))
rowdata$protein <- sapply(str_split(rowdata$ptm, pattern=" "),function(x) x[1])
ptm_y_assay <- SummarizedExperiment(assays=as.matrix(ptm_x_assay), rowData=rowdata, colData=colData(pe))
pe <- addAssay(pe, ptm_y_assay, "ptmRel")

4.2 Hypothesistest for each contrast

pe <- msqrob2::msqrob(object = pe, i = "ptmRel", formula = ~condition*subset, robust=TRUE, overwrite = T)
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps

## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
rowData(pe[["ptmRel"]])$msqrobModels[[2]] %>%
                  getCoef
##        (Intercept)         conditionA            subsety conditionA:subsety 
##        -0.20364594         0.02035259        -0.03977042         0.02892887
contrasts <- c("conditionA", "conditionA + conditionA:subsety", "conditionA:subsety", "conditionA + 0.5 * conditionA:subsety", "conditionA + 0.6666667 * conditionA:subsety")
L <- makeContrast(c("conditionA = 0",
                    "conditionA + conditionA:subsety = 0",
                    "conditionA:subsety = 0",
                    "conditionA + 0.5 * conditionA:subsety = 0",
                    "conditionA + 0.6666667 * conditionA:subsety = 0"),   
                  parameterNames = rowData(pe[["ptmRel"]])$msqrobModels[[2]] %>%
                  getCoef %>%
                  names)
pe <- hypothesisTest(object = pe, i = "ptmRel", contrast = L, overwrite = T)

4.2.1 Volcanoplot

volcanos <- list()
for (contrast in contrasts){
library(plotly)
volcanos[[contrast]] <- 
  rowData(pe[["ptmRel"]])[[contrast]]%>%
  ggplot(aes(x = logFC, y = -log10(pval), 
             color = adjPval < 0.05,
             annotation=rowData(pe[["ptmRel"]])[,3])) +
  geom_point(cex = 2.5) +
  scale_color_manual(values = alpha(c("black", "red"), 0.5)) + theme_minimal() +
  ylab("-log10(pvalue)") +
  ggtitle(contrast)
}
volcanos
## $conditionA
## Warning: Removed 68 rows containing missing values (geom_point).

## 
## $`conditionA + conditionA:subsety`
## Warning: Removed 68 rows containing missing values (geom_point).

## 
## $`conditionA:subsety`
## Warning: Removed 68 rows containing missing values (geom_point).

## 
## $`conditionA + 0.5 * conditionA:subsety`
## Warning: Removed 68 rows containing missing values (geom_point).

## 
## $`conditionA + 0.6666667 * conditionA:subsety`
## Warning: Removed 68 rows containing missing values (geom_point).

4.2.2 Heatmap

We first select the names of the ptms that were declared signficant.

4.2.3 Significant ptms for each contrast

tables <- list()
for (contrast in contrasts){
sigTable <- rowData(pe[["ptmRel"]])[[contrast]]
if(nrow(sigTable %>%
  na.exclude %>%
  filter(adjPval<0.05)) > 0){
sigTable <- sigTable %>%
  na.exclude %>%
  filter(adjPval<0.05) %>%
  arrange(pval) %>%
  mutate(
    se = format(se, digits = 2), 
    df = format(df, digits =2),
    t = format(t, digits = 2),
    adjPval = format(adjPval, digits = 3),
    rank = 1:length(logFC) 
  ) 
sigTable_print <- sigTable %>% mutate(peptidoform = rownames(sigTable)) %>% gt() %>% tab_header(title = md(contrast))
tables[[contrast]] <- sigTable
}
}
knitr::kable(tables)
logFC se df t pval adjPval rank
sp|P01019|ANGT_HUMAN (Oxidation (M)) 105 -3.2887952 0.16 4.8 -20.8 0.0000068 0.00193 1
sp|P10909|CLUS_HUMAN (Phospho (STY)) 210 -1.6735301 0.33 32.9 -5.1 0.0000128 0.00193 2
sp|P10451|OSTP_HUMAN (Oxidation (M)) 284 -0.7650361 0.17 82.4 -4.6 0.0000144 0.00193 3
sp|P02765|FETUA_HUMAN (Oxidation (M)) 321 -1.2172094 0.29 68.9 -4.1 0.0000962 0.00969 4
sp|O94769|ECM2_HUMAN (Oxidation (M)) 76 -0.6949871 0.18 73.2 -3.9 0.0001927 0.01366 5
sp|P05060|SCG1_HUMAN (Phospho (STY)) 317 -0.8350259 0.21 62.4 -3.9 0.0002034 0.01366 6
sp|O94769|ECM2_HUMAN (Phospho (STY)) 75 -0.7307004 0.19 80.5 -3.8 0.0002810 0.01607 7
sp|P05060|SCG1_HUMAN (Phospho (STY)) 311 -0.7705091 0.20 81.8 -3.8 0.0003190 0.01607 8
sp|P62328|TYB4_HUMAN (Acetyl (Protein N-term)) 1 0.8411341 0.18 13.0 4.8 0.0003640 0.01630 9
sp|O94769|ECM2_HUMAN (Phospho (STY)) 245 0.4363289 0.12 68.0 3.6 0.0005450 0.02155 10
sp|P01042|KNG1_HUMAN (Phospho (STY)) 275 -1.3223296 0.35 33.2 -3.8 0.0005882 0.02155 11
sp|P10451|OSTP_HUMAN (Phospho (STY)) 280 -1.0756663 0.31 84.1 -3.5 0.0006994 0.02349 12
logFC se df t pval adjPval rank
sp|P10909|CLUS_HUMAN (Phospho (STY)) 210 -2.363970 0.52 32.9 -4.6 0.0000680 0.021 1
sp|P01019|ANGT_HUMAN (Oxidation (M)) 105 -2.440847 0.21 4.8 -11.7 0.0001041 0.021 2
logFC se df t pval adjPval rank
sp|P01019|ANGT_HUMAN (Oxidation (M)) 105 -2.0683715 0.10 4.8 -19.8 0.0000086 0.00349 1
sp|O94769|ECM2_HUMAN (Oxidation (M)) 76 -0.6015157 0.16 73.2 -3.9 0.0002451 0.03315 2
sp|P07197|NFM_HUMAN (Phospho (STY)) 736 -1.4251552 0.33 23.6 -4.3 0.0002739 0.03315 3
sp|Q14515|SPRL1_HUMAN (Oxidation (M)) 276 -1.5631381 0.38 29.1 -4.1 0.0003290 0.03315 4
logFC se df t pval adjPval rank
sp|P01019|ANGT_HUMAN (Oxidation (M)) 105 -2.4751795 0.11 4.8 -21.6 0.0000058 0.00232 1
sp|O94769|ECM2_HUMAN (Oxidation (M)) 76 -0.6326728 0.15 73.2 -4.3 0.0000450 0.00906 2
sp|P07197|NFM_HUMAN (Phospho (STY)) 736 -1.4166206 0.32 23.6 -4.4 0.0001969 0.02640 3
sp|P10451|OSTP_HUMAN (Oxidation (M)) 284 -0.5165387 0.14 82.4 -3.8 0.0002620 0.02640 4
sp|Q14515|SPRL1_HUMAN (Oxidation (M)) 276 -1.4812379 0.38 29.1 -3.9 0.0005106 0.03572 5
sp|O94769|ECM2_HUMAN (Phospho (STY)) 75 -0.5560719 0.16 80.5 -3.6 0.0006166 0.03572 6
sp|P02765|FETUA_HUMAN (Oxidation (M)) 321 -0.8730036 0.24 68.9 -3.6 0.0006205 0.03572 7
sp|P01042|KNG1_HUMAN (Phospho (STY)) 275 -1.0204569 0.28 33.2 -3.7 0.0008739 0.04240 8
sp|P05060|SCG1_HUMAN (Phospho (STY)) 311 -0.5699020 0.17 81.8 -3.4 0.0009469 0.04240 9
a = "sp|Q16610|ECM1_HUMAN (Phospho (STY)) 80"
b = "sp|P13611|CSPG2_HUMAN (Phospho (STY)) 2115"
tables <- list()
for (contrast in contrasts){
sigTable <- rowData(pe[["ptmRel"]])[[contrast]]
tables[[contrast]] <- sigTable %>% rownames_to_column("ptm") %>% filter(ptm==a|ptm==b)
}
knitr::kable(tables)
ptm logFC se df t pval adjPval
sp|P13611|CSPG2_HUMAN (Phospho (STY)) 2115 NA NA NA NA NA NA
sp|Q16610|ECM1_HUMAN (Phospho (STY)) 80 -0.2646351 0.1593109 29.40381 -1.661124 0.1073162 0.9935778
ptm logFC se df t pval adjPval
sp|P13611|CSPG2_HUMAN (Phospho (STY)) 2115 NA NA NA NA NA NA
sp|Q16610|ECM1_HUMAN (Phospho (STY)) 80 -0.0540587 0.1428702 29.40381 -0.3783766 0.707869 0.9965335
ptm logFC se df t pval adjPval
sp|P13611|CSPG2_HUMAN (Phospho (STY)) 2115 NA NA NA NA NA NA
sp|Q16610|ECM1_HUMAN (Phospho (STY)) 80 0.2105764 0.2139903 29.40381 0.9840464 0.3331211 0.9521619
ptm logFC se df t pval adjPval
sp|P13611|CSPG2_HUMAN (Phospho (STY)) 2115 NA NA NA NA NA NA
sp|Q16610|ECM1_HUMAN (Phospho (STY)) 80 -0.1593469 0.1069951 29.40381 -1.489291 0.1470595 0.7317265
ptm logFC se df t pval adjPval
sp|P13611|CSPG2_HUMAN (Phospho (STY)) 2115 NA NA NA NA NA NA
sp|Q16610|ECM1_HUMAN (Phospho (STY)) 80 -0.1242508 0.1090502 29.40381 -1.139391 0.2637373 0.7749193

5 PLOTS

5.1 Lineplots

5.1.1 PTM - level

ptm_list1 <- c()
for (contrast in contrasts){
sigTable <- rowData(pe[["ptmRel"]])[[contrast]] %>% filter(adjPval<0.05)
ptm_list1 <- c(ptm_list1, rownames(sigTable))
}
ptm_list1 <- unique(ptm_list1)

plots <- list()
for (i in ptm_list1){
prot_ = str_split(i, " ")[[1]][1]
site_ = str_split_fixed(i, " ", 2)[,2]

pepform_df <- longFormat(pe[,,"pepformRel"], rowvars = c("Leading.razor.protein", "ptm"), colvars = c("condition", "subset")) %>% as.data.frame()
pepform_df <- pepform_df %>% filter(Leading.razor.protein == prot_)
pepform_df <- pepform_df %>% filter(grepl(site_, ptm, fixed = T))
pepform_df$FeatureType <- "Peptide"
pepform_df <- pepform_df %>% arrange(condition, subset, rowname)

ptm_df <- longFormat(pe[,,"ptmRel"], rowvars = c("protein", "ptm"), colvars = c("condition", "subset")) %>% as.data.frame()
ptm_df <- ptm_df %>% filter(ptm == i)
ptm_df$FeatureType <- "PTM"
ptm_df <- ptm_df %>% arrange(condition, subset, rowname)

ptm_estimate <- ptm_df %>% select(c("primary", "colname", "condition", "subset")) %>%
                           mutate(rowname = paste(ptm_df$rowname, "estimate"),
                                  ptm = paste(ptm_df$ptm, "estimate"),
                                  Protein = paste(ptm_df$Protein, "estimate"),
                                  FeatureType = "PTM_estimated",
                                  value = NA,
                                  assay = "model")
fixeffects <- rowData(pe[["ptmRel"]])$msqrobModels[[unique(ptm_df$ptm)]] %>% getCoef
ptm_estimate[ptm_estimate$condition=="B"&(ptm_estimate$subset=="x"),]$value <- 
                                                    fixeffects[["(Intercept)"]]
ptm_estimate[ptm_estimate$condition=="B"&(ptm_estimate$subset=="y"),]$value <- 
   fixeffects[["(Intercept)"]] + fixeffects[["subsety"]]
ptm_estimate[ptm_estimate$condition=="A"&(ptm_estimate$subset=="x"),]$value <- 
   fixeffects[["(Intercept)"]] + fixeffects[["conditionA"]]
ptm_estimate[ptm_estimate$condition=="A"&(ptm_estimate$subset=="y"),]$value <- 
   fixeffects[["(Intercept)"]] + fixeffects[["conditionA"]] + fixeffects[["conditionA:subsety"]] + fixeffects[["subsety"]]

plot_df <- rbindlist(list(pepform_df, ptm_df, ptm_estimate), fill = TRUE)
plot_points <- rbindlist(list(pepform_df, ptm_df), fill = TRUE)

plot_df$primary <- forcats::fct_inorder(plot_df$primary)

plots[[i]] <- plot_df %>% ggplot() +
  geom_line(aes(x = primary, y = value , group = rowname, color = FeatureType), size = 2) +
  geom_point(data = plot_points, aes(x = primary, y = value , group = rowname, 
                                     color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(43.5, 17.5, 56.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
  scale_colour_manual(values = c("Peptide" = "#C3C3C3", "PTM" = "palegreen3",
                                 "PTM_estimated" = "slateblue3")) +
  labs(title = i, x = "Sample", y = "Intensity") +
  theme_light() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 10),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=19),
        axis.title.y = element_text(size = 16),
        axis.title.x = element_text(size = 16),
        title = element_text(size = 19),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0.1)) +
  annotate("text", x = 24, y = 6.5, label = "B", size = 8) +
  annotate("text", x = 70, y = 6.5, label = "A", size = 8) +
  annotate("text", x = 9, y = 6, label = "x", size = 7) +
  annotate("text", x = 30, y = 6, label = "y", size = 7) +
  annotate("text", x = 49.5, y = 6, label = "x", size = 7) +
  annotate("text", x = 73, y = 6, label = "y", size = 7) +
  ylim(-6, 6.5)

}
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plots
## $`sp|O94769|ECM2_HUMAN (Phospho (STY)) 75`
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## 
## $`sp|O94769|ECM2_HUMAN (Oxidation (M)) 76`
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## 
## $`sp|O94769|ECM2_HUMAN (Phospho (STY)) 245`
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## 
## $`sp|P01019|ANGT_HUMAN (Oxidation (M)) 105`
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## 
## $`sp|P01042|KNG1_HUMAN (Phospho (STY)) 275`
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## 
## $`sp|P02765|FETUA_HUMAN (Oxidation (M)) 321`
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## 
## $`sp|P05060|SCG1_HUMAN (Phospho (STY)) 311`
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## 
## $`sp|P10909|CLUS_HUMAN (Phospho (STY)) 210`
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## 
## $`sp|P62328|TYB4_HUMAN (Acetyl (Protein N-term)) 1`
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## 
## $`sp|P07197|NFM_HUMAN (Phospho (STY)) 736`
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## 
## $`sp|Q14515|SPRL1_HUMAN (Oxidation (M)) 276`
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5.1.2 Peptidoform level

pepf_list1 <- c()
for (contrast in contrasts){
sigTable <- rowData(pe[["pepformRel"]])[[contrast]] %>% filter(adjPval<0.05)
pepf_list1 <- c(pepf_list1, rownames(sigTable))
}
pepf_list1 <- unique(pepf_list1)

plots <- list()
for (i in pepf_list1){
prot_ = rowData(pe[["pepformRel"]])[i,][["Leading.razor.protein"]]
site_ = rowData(pe[["pepformRel"]])[i,][["mod"]]
sites_ = strsplit(site_, ", ")[[1]]
for (site_ in sites_){
ptm_ = paste(prot_, site_, collapse = " ")
print(ptm_)

pepform_df <- longFormat(pe[,,"pepformRel"], rowvars = c("Leading.razor.protein", "ptm"), colvars = c("condition", "subset")) %>% as.data.frame()
pepform_df <- pepform_df %>% filter(Leading.razor.protein == prot_)
pepform_df <- pepform_df %>% filter(grepl(site_, ptm, fixed = T))
pepform_df$FeatureType <- "Peptide"
pepform_df[pepform_df$rowname==i,]$FeatureType <- "SignificantPeptide"
pepform_df <- pepform_df %>% arrange(condition, subset, rowname)

ptm_df <- longFormat(pe[,,"ptmRel"], rowvars = c("protein", "ptm"), colvars = c("condition", "subset")) %>% as.data.frame()
ptm_df <- ptm_df %>% filter(ptm == ptm_)
ptm_df$FeatureType <- "PTM"
ptm_df <- ptm_df %>% arrange(condition, subset, rowname)

ptm_estimate <- ptm_df %>% select(c("primary", "colname", "condition", "subset")) %>%
                           mutate(rowname = paste(ptm_df$rowname, "estimate"),
                                  ptm = paste(ptm_df$ptm, "estimate"),
                                  Protein = paste(ptm_df$Protein, "estimate"),
                                  FeatureType = "PTM_estimated",
                                  value = NA,
                                  assay = "model")
fixeffects <- rowData(pe[["ptmRel"]])$msqrobModels[[unique(ptm_df$ptm)]] %>% getCoef
ptm_estimate[ptm_estimate$condition=="B"&(ptm_estimate$subset=="x"),]$value <- 
                                                    fixeffects[["(Intercept)"]]
ptm_estimate[ptm_estimate$condition=="B"&(ptm_estimate$subset=="y"),]$value <- 
   fixeffects[["(Intercept)"]] + fixeffects[["subsety"]]
ptm_estimate[ptm_estimate$condition=="A"&(ptm_estimate$subset=="x"),]$value <- 
   fixeffects[["(Intercept)"]] + fixeffects[["conditionA"]]
ptm_estimate[ptm_estimate$condition=="A"&(ptm_estimate$subset=="y"),]$value <- 
   fixeffects[["(Intercept)"]] + fixeffects[["conditionA"]] + fixeffects[["conditionA:subsety"]]+ fixeffects[["subsety"]]


plot_df <- rbindlist(list(pepform_df, ptm_df, ptm_estimate), fill = TRUE)
plot_points <- rbindlist(list(pepform_df, ptm_df), fill = TRUE)

# plot_df[plot_df$FeatureType == 'PTM'][['FeatureType']] <- "PTM Summarized"
# plot_df[plot_df$FeatureType != 'PTM'][['FeatureType']] <- "PTM Feature"
plot_df$primary <- forcats::fct_inorder(plot_df$primary)

plots[[paste(i, site_)]] <- plot_df %>% ggplot() +
  geom_line(aes(x = primary, y = value , group = rowname, color = FeatureType), size =2) +
  geom_point(data = plot_points, aes(x = primary, y = value , group = rowname, color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(43.5, 17.5, 56.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
  scale_colour_manual(values = c("Peptide" = "#C3C3C3", "PTM" = "palegreen3", 
                                 "SignificantPeptide" = "violetred1",
                                 "PTM_estimated" = "lightgoldenrod")) +
  labs(title = paste(i, ptm_), x = "Sample", y = "Intensity") +
  theme_light() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 10),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=19),
        axis.title.y = element_text(size = 16),
        axis.title.x = element_text(size = 16),
        title = element_text(size = 19),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0.1)) +
  annotate("text", x = 24, y = 6.5, label = "B", size = 8) +
  annotate("text", x = 70, y = 6.5, label = "A", size = 8) +
  annotate("text", x = 9, y = 6, label = "x", size = 7) +
  annotate("text", x = 30, y = 6, label = "y", size = 7) +
  annotate("text", x = 49.5, y = 6, label = "x", size = 7) +
  annotate("text", x = 73, y = 6, label = "y", size = 7) +
  ylim(-6, 6.5)
}
}
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## $`_LPIVNFDYS(Phospho (STY))M(Oxidation (M))EEK_ (Phospho (STY)) 75`
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## 
## $`_AAM(Oxidation (M))VGMLANFLGFR_ (Oxidation (M)) 105`
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## 
## $`_VS(Phospho (STY))REFHSHEFHSHEDM(Oxidation (M))LVVDPK_ (Phospho (STY)) 270`
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## 
## $`_EPQDTYHYLPFS(Phospho (STY))LPHR_ (Phospho (STY)) 210`
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---
title: "phosphoDataset msqrobPTM without GP"
output:
  html_document:
    code_download: yes
    theme: cosmo
    toc: yes
    toc_depth: 4
    toc_float:
      collapsed: yes
    highlight: tango
    number_sections: yes
    df_print: paged
  pdf_document:
    toc: yes
    toc_depth: '4'
---

```{r, setup, include=FALSE}
knitr::opts_knit$set(root.dir = "C:/Users/Nina/OneDrive - UGent/Documenten/Doctoraat/msqrobPTM paper/biological phospho dataset/")
```

```{r message=FALSE}
library(QFeatures)
library(msqrob2)
library(dplyr)
library(tidyr)
library(tibble)
library(gt)
library(plotly)
library(stageR)
library(poolr)
library(RColorBrewer)
library(seqinr)
library(stringr)
library(ExploreModelMatrix)
library(data.table)
```



# Import data


```{r}
df <- read.csv("evidence.txt",header=TRUE, sep = ",")
#I can ignore the sigma proteins (those were just used for QC purposes)
df <- df %>% filter(!grepl("Sigma", df$Proteins))
#filter out phospho sites with less than 75% probability
#(other filtering options are possible)
filter_rows <- sapply(df$Phospho..STY..Probabilities, function(x){
  if (!x==""){
    prob <- str_extract_all(x,  "(?<=\\().+?(?=\\))")[[1]]
    if (!any(as.double(prob) > 0.75)){return(which(df$Phospho..STY..Probabilities == x))}
  }
}, USE.NAMES = F)
filter_rows <- unique(unlist(filter_rows))
nrow(df)
df <- df[-filter_rows,]
nrow(df)
#Now get format into wide format
df_wide <- pivot_wider(df, id_cols = c("Sequence", "Modifications", "Modified.sequence", "Proteins", "Leading.proteins",
                                        "Reverse", "Potential.contaminant", "Protein.group.IDs", "Leading.razor.protein"),
                                                                          #Is dat hier de beste manier om met 
                                                                          #gedupliceerde features om te gaan?
                       names_from = "Raw.file", names_prefix = "Intensity_", values_from = "Intensity", values_fn = max)
```


```{r}
ecols <- grep("Intensity",colnames(df_wide))
#order dataframe by protein, for the normalisation step
df_wide = df_wide[order(df_wide$Leading.razor.protein),]
pe <- readQFeatures(df_wide,ecol= ecols,name="peptidoformRaw")
```

```{r}
rownames(pe[["peptidoformRaw"]]) <- rowData(pe[["peptidoformRaw"]])$Modified.sequence
```

## Experimental Layout

```{r}
metadata <- read.csv("Experimental Design.csv")
```

```{r}
colData(pe)$file <- sapply(str_split(rownames(colData(pe)), "_"), function(x) x[[2]])
metadata$File <- sub(x=metadata$File, pattern=".raw",replacement = "")
colData(pe)$condition <- sapply(colData(pe)$file, function(x){
  metadata[metadata$File==x,]$Condition
}, USE.NAMES = F)
colData(pe)$subset <- sapply(colData(pe)$file, function(x){
  metadata[metadata$File==x,]$Subset
}, USE.NAMES = F)
colData(pe)$condition <- case_when(grepl("A", colData(pe)$condition) ~ "A",
                                        TRUE ~ "B")
colData(pe)
```


```{r}
MSnbase::plotNA(assay(pe[["peptidoformRaw"]])) +
  xlab("Peptide index (ordered by data completeness)")
```


# Preprocessing


```{r}
rowData(pe[["peptidoformRaw"]])$nNonZero <- rowSums(assay(pe[["peptidoformRaw"]]) > 0, na.rm = T)
pe <- zeroIsNA(pe, i = "peptidoformRaw")
pe <- filterFeatures(pe, ~Reverse != "+")
pe <- filterFeatures(pe, ~Potential.contaminant != "+")
pe <- logTransform(pe, base = 2, i = "peptidoformRaw", name = "peptidoformLog")
pe <- pe[rowData(pe[["peptidoformRaw"]])$nNonZero>2,,]
pe <- QFeatures::normalize(pe, method = "center.median", i = "peptidoformLog", name = "peptidoform")
colData(pe[["peptidoform"]]) <- colData(pe)
```


## Normalisation via robust summarisation


```{r,warning=FALSE}
pe <- aggregateFeatures(pe,
 i = "peptidoform",
 fcol = "Leading.razor.protein",
 na.rm = TRUE,
 name = "proteinRobust",
 fun = MsCoreUtils::robustSummary)
```

```{r}
#Only take pepforms that have a parent protein
#(when there is no global profiling dataset, this will be all peptidoforms)
pepWithProtein <- rowData(pe[["peptidoform"]])$Proteins %in% rownames(pe[["proteinRobust"]])
pePepWithProtein  <- pe[["peptidoform"]][pepWithProtein,]
pe <- addAssay(pe,pePepWithProtein,"pepformRel")
#normalisation for protein abundance step
assay(pe[["pepformRel"]]) <- assay(pe[["pepformRel"]]) - assay(pe[["proteinRobust"]])[rowData(pe[["pepformRel"]])$Proteins,colnames(assay(pe[["pepformRel"]]))]
boxplot(assay(pe[["pepformRel"]]))
```


# Differential peptidoform usage (DPFU)

## Hypothesistest for each contrast

```{r}
colData(pe)$condition <- relevel(as.factor(colData(pe)$condition), ref = "B")
colData(pe)$subset <- as.factor(colData(pe)$subset)
```

```{r}
pe <- msqrob2::msqrob(object = pe, i = "pepformRel", formula = ~condition*subset, robust=FALSE)
rowData(pe[["pepformRel"]])$msqrobModels[[2]] %>%
                  getCoef
```


```{r}
fraction_of_ys <- (colData(pe) %>% as_tibble() %>% filter(subset == "y") %>% nrow()) / nrow(colData(pe))
contrasts <- c("conditionA", "conditionA + conditionA:subsety", "conditionA:subsety", "conditionA + 0.5 * conditionA:subsety", "conditionA + 0.6666667 * conditionA:subsety")
L <- makeContrast(c("conditionA = 0",
                    "conditionA + conditionA:subsety = 0",
                    "conditionA:subsety = 0",
                    "conditionA + 0.5 * conditionA:subsety = 0",
                    "conditionA + 0.6666667 * conditionA:subsety = 0"),
                  parameterNames = rowData(pe[["pepformRel"]])$msqrobModels[[2]] %>%
                  getCoef %>%
                  names)
pe <- hypothesisTest(object = pe, i = "pepformRel", contrast = L, overwrite = T)
```


### Volcanoplot 

```{r}
volcano <- list()
for (contrast in contrasts){
volcano[[contrast]] <- rowData(pe[["pepformRel"]])[[contrast]]%>%
            ggplot(aes(x = logFC, y = -log10(pval), 
                   color = adjPval < 0.05,
                   annotation=rowData(pe[["pepformRel"]])[,3])) +
  geom_point(cex = 2.5) +
  scale_color_manual(values = alpha(c("black", "red"), 0.5)) + 
  theme_minimal() +
  ylab("-log10(pvalue)") +
  ggtitle(contrast)
}
volcano
```

```{r eval=FALSE, fig.height=4, fig.width=12, include=FALSE}
for(contrast in contrasts){
sigNames <- rowData(pe[["pepformRel"]])[[contrast]] %>%
 rownames_to_column("peptidoform") %>%
 filter(adjPval<0.05) %>%
 pull(peptidoform)
heatmap(assay(pe[["pepformRel"]])[sigNames, ], main = contrast)
}
```

### Significant peptidoforms for each contrast


```{r}
tables <- list()
for (contrast in contrasts){
sigTable <- rowData(pe[["pepformRel"]])[[contrast]]
if(nrow(sigTable %>%
  na.exclude %>%
  filter(adjPval<0.05))>0){
sigTable <- sigTable %>%
  na.exclude %>%
  filter(adjPval<0.05) %>%
  arrange(pval) %>%
  mutate(
    se = format(se, digits = 2), 
    df = format(df, digits =2),
    t = format(t, digits = 2),
    adjPval = format(adjPval, digits = 3),
    rank = 1:length(logFC) 
  ) 
sigTable_print <- sigTable %>% mutate(peptidoform = rownames(sigTable)) %>% gt() %>% tab_header(title = md(contrast))
tables[[contrast]] <- sigTable
}
}
knitr::kable(tables)
```

```{r eval=FALSE, include=FALSE}
tables_toCsv <- do.call("rbind", tables)
tables_toCsv <- tables_toCsv %>% rownames_to_column("contrast")
pepforms <- sapply(unique(tables_toCsv$contrast), function(x){rownames(tables[[x]])}, USE.NAMES = F)
tables_toCsv$Peptidoform <- pepforms
write.csv(tables_toCsv, file = "C:/Users/Nina/OneDrive - UGent/Documenten/Doctoraat/msqrobPTM/PhosphoDataset /significance_tables_peptidoform.csv")
```


# DPTM

## ptm summarisatie

### Determine location of ptm

```{r}
fasta <- "human.fasta"
parsed_fasta <- read.fasta(file = fasta, seqtype = "AA", as.string = T)
```

  
```{r}
#modified sequence column contains _ that does not matter, but hinders location determining
rowData(pe[["pepformRel"]])$modified_sequence <- gsub("_", "", rowData(pe[["pepformRel"]])$Modified.sequence)
```

  

```{r}
get_ptm_location <- function(feature, data, fasta, mod_column = "Modifications", 
                             peptide_seq_column = "Sequence", mod_seq_column = "modified_sequence", 
                             protein_column = "Leading.razor.protein", split = ",", collapse = ", "){
  prot <- data[feature,][[protein_column]]
  pep_seq <- data[feature,][[peptide_seq_column]]
  mod_seq <- data[feature,][[mod_seq_column]]
  prot_seq <- fasta[[prot]][1]

  #find location of peptide in protein
  #add -1 here, so that the addition of the location later on is correct
  pep_location <- unlist(lapply(gregexpr(pattern = pep_seq, prot_seq), min)) - 1
  final_mod <- c()
  j <- mod_seq
  #go over the modifications inside the modified sequence
  for(mod in regmatches(mod_seq, gregexpr("\\(.*?\\)\\)", mod_seq, perl=T))[[1]]){
    #find location of modification in peptide
    mod_location <- unlist(lapply(gregexpr(pattern = mod, j, fixed = T), min))
    #get location in protein (-1 because else it gives you the location after because of the presence of the modification in the string)
    location <- mod_location + pep_location -1
    #add location to modification
    mod_ <- paste(mod, location)
    #save modification
    final_mod <- c(final_mod, mod_)
    #now remove current modification from the sequence, so that we can continue to the next mod
    str_sub(j, mod_location, nchar(mod)+mod_location-1) <- ""
  }
  return(paste(final_mod, collapse = collapse))
}
```

```{r}
rowData(pe[["pepformRel"]])$mod <- sapply(rownames(rowData(pe[["pepformRel"]])), function(x){
    get_ptm_location(x, rowData(pe[["pepformRel"]]), parsed_fasta)
}, USE.NAMES = F)
```


```{r}
#Add ptm variable = protein + modification
rowData(pe[["pepformRel"]])$ptm <- ifelse(rowData(pe[["pepformRel"]])$mod != "",
                                               paste(rowData(pe[["pepformRel"]])$Leading.razor.protein, rowData(pe[["pepformRel"]])$mod, sep="_"), 
                                               "")
```

### Get ptm level intensity matrix

Get all unique ptms present in the dataset (all protein - modification - location combinations)

```{r}
prots <- unique(rowData(pe[["pepformRel"]])$Leading.razor.protein)
#Do for each protein
ptms <- sapply(prots, function(i) {
  pe_sub <- pe[["pepformRel"]][grepl(i, rowData(pe[["pepformRel"]])$Leading.razor.protein, fixed = T),]
  #pe_sub <- filterFeatures(pe,~grepl(Leading.razor.protein,pattern=i,fixed = T))
  #Get all unique modification present on that protein
  mods <- unique(unlist(strsplit(rowData(pe_sub)$mod, split = ", ", fixed = TRUE)))
  #Add protein info to mods
  ptm <- paste(rep(i, length(mods)), mods)
  #return all the protein-mods combinations
  ptm
})
ptms <- as.vector(unlist(ptms))
```



```{r}
#For each ptm do
ptm_x_assay <- sapply(seq(1:length(ptms)), function(i){ 
  x <- ptms[i]
  #Get current protein and mod from ptm
  prot <- str_split(x, " ", 2)[[1]][1]
  current_ptm <- str_split(x, " ", 2)[[1]][2]
  #filter on that protein and on that mod to obtain all peptidoforms that correspond to the ptm
  pe_sub <- pe[["pepformRel"]][grepl(prot, rowData(pe[["pepformRel"]])$Leading.razor.protein, fixed = T),]
  #pe_sub <- filterFeatures(pe,~grepl(Leading.razor.protein,pattern=prot, fixed = T))
  ptm_sub <- pe_sub[grepl(current_ptm, rowData(pe_sub)$mod, fixed = T),]
  #ptm_sub <- filterFeatures(pe_sub,~grepl(mod,pattern=current_ptm, fixed=T))[["peptidoformNorm"]]
  #Get intensity values of those peptidoforms
  y <- assay(ptm_sub)
  #And summarise them to 1 row of intensity values: 1 value per sample for that ptm
  ptm_y <- try(MsCoreUtils::robustSummary(y), silent = T)
  if (is(ptm_y, "try-error")){
    ptm_y <- rep(NA, ncol(y))}
  ptm_y
})
#Then we get the intensity assay on ptm level
ptm_x_assay <- t(ptm_x_assay)
rownames(ptm_x_assay) <- ptms
```

### Add to QFeatures object

Filter out ptms with all zero intensities

```{r}
print(paste(nrow(ptm_x_assay), "ptms before filtering"))
filtering <- rowSums(ptm_x_assay != 0, na.rm=TRUE) > 0 
ptm_x_assay <- ptm_x_assay[filtering,]
print(paste(nrow(ptm_x_assay), "ptms after filtering"))
```

```{r}
all(rownames(colData(pe)) == colnames(ptm_x_assay))
rowdata <- data.frame(ptm = rownames(ptm_x_assay))
rowdata$protein <- sapply(str_split(rowdata$ptm, pattern=" "),function(x) x[1])
ptm_y_assay <- SummarizedExperiment(assays=as.matrix(ptm_x_assay), rowData=rowdata, colData=colData(pe))
```


```{r}
pe <- addAssay(pe, ptm_y_assay, "ptmRel")
```



## Hypothesistest for each contrast


```{r}
pe <- msqrob2::msqrob(object = pe, i = "ptmRel", formula = ~condition*subset, robust=TRUE, overwrite = T)
rowData(pe[["ptmRel"]])$msqrobModels[[2]] %>%
                  getCoef
```



```{r}
contrasts <- c("conditionA", "conditionA + conditionA:subsety", "conditionA:subsety", "conditionA + 0.5 * conditionA:subsety", "conditionA + 0.6666667 * conditionA:subsety")
L <- makeContrast(c("conditionA = 0",
                    "conditionA + conditionA:subsety = 0",
                    "conditionA:subsety = 0",
                    "conditionA + 0.5 * conditionA:subsety = 0",
                    "conditionA + 0.6666667 * conditionA:subsety = 0"),   
                  parameterNames = rowData(pe[["ptmRel"]])$msqrobModels[[2]] %>%
                  getCoef %>%
                  names)
pe <- hypothesisTest(object = pe, i = "ptmRel", contrast = L, overwrite = T)
```



### Volcanoplot 


```{r}
volcanos <- list()
for (contrast in contrasts){
library(plotly)
volcanos[[contrast]] <- 
  rowData(pe[["ptmRel"]])[[contrast]]%>%
  ggplot(aes(x = logFC, y = -log10(pval), 
             color = adjPval < 0.05,
             annotation=rowData(pe[["ptmRel"]])[,3])) +
  geom_point(cex = 2.5) +
  scale_color_manual(values = alpha(c("black", "red"), 0.5)) + theme_minimal() +
  ylab("-log10(pvalue)") +
  ggtitle(contrast)
}
volcanos
```

### Heatmap

We first select the names of the ptms that were declared signficant.

```{r eval=FALSE, include=FALSE}
for(contrast in contrasts){
sigNames <- rowData(pe[["ptmRel"]])[[contrast]] %>%
 rownames_to_column("ptmRel") %>%
 filter(adjPval<0.05) %>%
 pull(ptm)
heatmap(assay(pe[["ptmRel"]])[sigNames, ], main = contrast)
}
```


### Significant ptms for each contrast


```{r}
tables <- list()
for (contrast in contrasts){
sigTable <- rowData(pe[["ptmRel"]])[[contrast]]
if(nrow(sigTable %>%
  na.exclude %>%
  filter(adjPval<0.05)) > 0){
sigTable <- sigTable %>%
  na.exclude %>%
  filter(adjPval<0.05) %>%
  arrange(pval) %>%
  mutate(
    se = format(se, digits = 2), 
    df = format(df, digits =2),
    t = format(t, digits = 2),
    adjPval = format(adjPval, digits = 3),
    rank = 1:length(logFC) 
  ) 
sigTable_print <- sigTable %>% mutate(peptidoform = rownames(sigTable)) %>% gt() %>% tab_header(title = md(contrast))
tables[[contrast]] <- sigTable
}
}
knitr::kable(tables)
```

```{r eval=FALSE, include=FALSE}
tables_toCsv <- do.call("rbind", tables)
tables_toCsv <- tables_toCsv %>% rownames_to_column("rowname")
write.csv(tables_toCsv, file = "significance_tables_PTM.csv")
```


```{r}
a = "sp|Q16610|ECM1_HUMAN (Phospho (STY)) 80"
b = "sp|P13611|CSPG2_HUMAN (Phospho (STY)) 2115"
tables <- list()
for (contrast in contrasts){
sigTable <- rowData(pe[["ptmRel"]])[[contrast]]
tables[[contrast]] <- sigTable %>% rownames_to_column("ptm") %>% filter(ptm==a|ptm==b)
}
knitr::kable(tables)
```

# PLOTS

## Lineplots

### PTM - level

```{r, fig.width=18, fig.height=10}
ptm_list1 <- c()
for (contrast in contrasts){
sigTable <- rowData(pe[["ptmRel"]])[[contrast]] %>% filter(adjPval<0.05)
ptm_list1 <- c(ptm_list1, rownames(sigTable))
}
ptm_list1 <- unique(ptm_list1)

plots <- list()
for (i in ptm_list1){
prot_ = str_split(i, " ")[[1]][1]
site_ = str_split_fixed(i, " ", 2)[,2]

pepform_df <- longFormat(pe[,,"pepformRel"], rowvars = c("Leading.razor.protein", "ptm"), colvars = c("condition", "subset")) %>% as.data.frame()
pepform_df <- pepform_df %>% filter(Leading.razor.protein == prot_)
pepform_df <- pepform_df %>% filter(grepl(site_, ptm, fixed = T))
pepform_df$FeatureType <- "Peptide"
pepform_df <- pepform_df %>% arrange(condition, subset, rowname)

ptm_df <- longFormat(pe[,,"ptmRel"], rowvars = c("protein", "ptm"), colvars = c("condition", "subset")) %>% as.data.frame()
ptm_df <- ptm_df %>% filter(ptm == i)
ptm_df$FeatureType <- "PTM"
ptm_df <- ptm_df %>% arrange(condition, subset, rowname)

ptm_estimate <- ptm_df %>% select(c("primary", "colname", "condition", "subset")) %>%
                           mutate(rowname = paste(ptm_df$rowname, "estimate"),
                                  ptm = paste(ptm_df$ptm, "estimate"),
                                  Protein = paste(ptm_df$Protein, "estimate"),
                                  FeatureType = "PTM_estimated",
                                  value = NA,
                                  assay = "model")
fixeffects <- rowData(pe[["ptmRel"]])$msqrobModels[[unique(ptm_df$ptm)]] %>% getCoef
ptm_estimate[ptm_estimate$condition=="B"&(ptm_estimate$subset=="x"),]$value <- 
                                                    fixeffects[["(Intercept)"]]
ptm_estimate[ptm_estimate$condition=="B"&(ptm_estimate$subset=="y"),]$value <- 
   fixeffects[["(Intercept)"]] + fixeffects[["subsety"]]
ptm_estimate[ptm_estimate$condition=="A"&(ptm_estimate$subset=="x"),]$value <- 
   fixeffects[["(Intercept)"]] + fixeffects[["conditionA"]]
ptm_estimate[ptm_estimate$condition=="A"&(ptm_estimate$subset=="y"),]$value <- 
   fixeffects[["(Intercept)"]] + fixeffects[["conditionA"]] + fixeffects[["conditionA:subsety"]] + fixeffects[["subsety"]]

plot_df <- rbindlist(list(pepform_df, ptm_df, ptm_estimate), fill = TRUE)
plot_points <- rbindlist(list(pepform_df, ptm_df), fill = TRUE)

plot_df$primary <- forcats::fct_inorder(plot_df$primary)

plots[[i]] <- plot_df %>% ggplot() +
  geom_line(aes(x = primary, y = value , group = rowname, color = FeatureType), size = 2) +
  geom_point(data = plot_points, aes(x = primary, y = value , group = rowname, 
                                     color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(43.5, 17.5, 56.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
  scale_colour_manual(values = c("Peptide" = "#C3C3C3", "PTM" = "palegreen3",
                                 "PTM_estimated" = "slateblue3")) +
  labs(title = i, x = "Sample", y = "Intensity") +
  theme_light() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 10),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=19),
        axis.title.y = element_text(size = 16),
        axis.title.x = element_text(size = 16),
        title = element_text(size = 19),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0.1)) +
  annotate("text", x = 24, y = 6.5, label = "B", size = 8) +
  annotate("text", x = 70, y = 6.5, label = "A", size = 8) +
  annotate("text", x = 9, y = 6, label = "x", size = 7) +
  annotate("text", x = 30, y = 6, label = "y", size = 7) +
  annotate("text", x = 49.5, y = 6, label = "x", size = 7) +
  annotate("text", x = 73, y = 6, label = "y", size = 7) +
  ylim(-6, 6.5)

}
plots
```


### Peptidoform level



```{r, fig.width=18, fig.height=10}
pepf_list1 <- c()
for (contrast in contrasts){
sigTable <- rowData(pe[["pepformRel"]])[[contrast]] %>% filter(adjPval<0.05)
pepf_list1 <- c(pepf_list1, rownames(sigTable))
}
pepf_list1 <- unique(pepf_list1)

plots <- list()
for (i in pepf_list1){
prot_ = rowData(pe[["pepformRel"]])[i,][["Leading.razor.protein"]]
site_ = rowData(pe[["pepformRel"]])[i,][["mod"]]
sites_ = strsplit(site_, ", ")[[1]]
for (site_ in sites_){
ptm_ = paste(prot_, site_, collapse = " ")
print(ptm_)

pepform_df <- longFormat(pe[,,"pepformRel"], rowvars = c("Leading.razor.protein", "ptm"), colvars = c("condition", "subset")) %>% as.data.frame()
pepform_df <- pepform_df %>% filter(Leading.razor.protein == prot_)
pepform_df <- pepform_df %>% filter(grepl(site_, ptm, fixed = T))
pepform_df$FeatureType <- "Peptide"
pepform_df[pepform_df$rowname==i,]$FeatureType <- "SignificantPeptide"
pepform_df <- pepform_df %>% arrange(condition, subset, rowname)

ptm_df <- longFormat(pe[,,"ptmRel"], rowvars = c("protein", "ptm"), colvars = c("condition", "subset")) %>% as.data.frame()
ptm_df <- ptm_df %>% filter(ptm == ptm_)
ptm_df$FeatureType <- "PTM"
ptm_df <- ptm_df %>% arrange(condition, subset, rowname)

ptm_estimate <- ptm_df %>% select(c("primary", "colname", "condition", "subset")) %>%
                           mutate(rowname = paste(ptm_df$rowname, "estimate"),
                                  ptm = paste(ptm_df$ptm, "estimate"),
                                  Protein = paste(ptm_df$Protein, "estimate"),
                                  FeatureType = "PTM_estimated",
                                  value = NA,
                                  assay = "model")
fixeffects <- rowData(pe[["ptmRel"]])$msqrobModels[[unique(ptm_df$ptm)]] %>% getCoef
ptm_estimate[ptm_estimate$condition=="B"&(ptm_estimate$subset=="x"),]$value <- 
                                                    fixeffects[["(Intercept)"]]
ptm_estimate[ptm_estimate$condition=="B"&(ptm_estimate$subset=="y"),]$value <- 
   fixeffects[["(Intercept)"]] + fixeffects[["subsety"]]
ptm_estimate[ptm_estimate$condition=="A"&(ptm_estimate$subset=="x"),]$value <- 
   fixeffects[["(Intercept)"]] + fixeffects[["conditionA"]]
ptm_estimate[ptm_estimate$condition=="A"&(ptm_estimate$subset=="y"),]$value <- 
   fixeffects[["(Intercept)"]] + fixeffects[["conditionA"]] + fixeffects[["conditionA:subsety"]]+ fixeffects[["subsety"]]


plot_df <- rbindlist(list(pepform_df, ptm_df, ptm_estimate), fill = TRUE)
plot_points <- rbindlist(list(pepform_df, ptm_df), fill = TRUE)

# plot_df[plot_df$FeatureType == 'PTM'][['FeatureType']] <- "PTM Summarized"
# plot_df[plot_df$FeatureType != 'PTM'][['FeatureType']] <- "PTM Feature"
plot_df$primary <- forcats::fct_inorder(plot_df$primary)

plots[[paste(i, site_)]] <- plot_df %>% ggplot() +
  geom_line(aes(x = primary, y = value , group = rowname, color = FeatureType), size =2) +
  geom_point(data = plot_points, aes(x = primary, y = value , group = rowname, color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(43.5, 17.5, 56.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
  scale_colour_manual(values = c("Peptide" = "#C3C3C3", "PTM" = "palegreen3", 
                                 "SignificantPeptide" = "violetred1",
                                 "PTM_estimated" = "lightgoldenrod")) +
  labs(title = paste(i, ptm_), x = "Sample", y = "Intensity") +
  theme_light() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 10),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=19),
        axis.title.y = element_text(size = 16),
        axis.title.x = element_text(size = 16),
        title = element_text(size = 19),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0.1)) +
  annotate("text", x = 24, y = 6.5, label = "B", size = 8) +
  annotate("text", x = 70, y = 6.5, label = "A", size = 8) +
  annotate("text", x = 9, y = 6, label = "x", size = 7) +
  annotate("text", x = 30, y = 6, label = "y", size = 7) +
  annotate("text", x = 49.5, y = 6, label = "x", size = 7) +
  annotate("text", x = 73, y = 6, label = "y", size = 7) +
  ylim(-6, 6.5)
}
}
plots
```

